Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction TreesComputer Science Faculty Publications and Presentations
AbstractThe assessment of students’ metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, we built a fuzzy inference system (FIS) with the extracted rules as a rule base and classified the test dataset with the FIS. The extracted rules are evaluated using measures such as the overall efficiency and Kappa coefficient.
DescriptionThis article, originally published in the International Journal of Advanced Computer Science and Applications, is an open access article licensed under a Creative Commons Attribution 4.0 International License.
PublisherInternational Journal of Advanced Computer Science and Applications
Date of publication1-1-2016
Publisher CitationChristopher Taylor, Arun Kulkarni and Kouider Mokhtar, “Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction Trees” International Journal of Advanced Computer Science and Applications(IJACSA), 7(6), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070634 - See more at: http://thesai.org/Publications/ViewPaper?Volume=7&Issue=6&Code=IJACSA&SerialNo=34#sthash.xmI8JX7j.dpuf
Citation InformationChristopher Taylor, Arun D. Kulkarni and Kouider Mokhtari. "Knowledge Extraction from Metacognitive Reading Strategies Data Using Induction Trees" (2016)
Available at: http://works.bepress.com/arun-kulkarni/56/